Structural and functional characterization of FabG4 from Mycolicibacterium smegmatis.

The rise in antimicrobial resistance is a global health crisis and necessitates the development of novel strategies to treat infections. For example, in 2022 tuberculosis (TB) was the second leading infectious killer after COVID-19, with multi-drug-resistant strains of TB having an ∼40% fatality rate. Targeting essential biosynthetic pathways in pathogens has proven to be successful for the development of novel antimicrobial treatments. Fatty-acid synthesis (FAS) in bacteria proceeds via the type II pathway, which is substantially different from the type I pathway utilized in animals. This makes bacterial fatty-acid biosynthesis (Fab) enzymes appealing as drug targets. FabG is an essential FASII enzyme, and some bacteria, such as Mycobacterium tuberculosis, the causative agent of TB, harbor multiple homologs. FabG4 is a conserved, high-molecular-weight FabG (HMwFabG) that was first identified in M. tuberculosis and is distinct from the canonical low-molecular-weight FabG. Here, structural and functional analyses of Mycolicibacterium smegmatis FabG4, the third HMwFabG studied to date, are reported. Crystal structures of NAD+ and apo MsFabG4, along with kinetic analyses, show that MsFabG4 preferentially binds and uses NADH when reducing CoA substrates. As M. smegmatis is often used as a model organism for M. tuberculosis, these studies may aid the development of drugs to treat TB and add to the growing body of research that distinguish HMwFabGs from the archetypal low-molecular-weight FabG.

Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations.

Kinetic modeling has relied on using a tedious number of mathematical equations to describe molecular kinetics in interacting reactions. The long list of differential equations with associated abstract variables and parameters inevitably hinders readers' easy understanding of the models. However, the mathematical equations describing the kinetics of biochemical reactions can be exactly mapped to the dynamics of voltages and currents in simple electronic circuits wherein voltages represent molecular concentrations and currents represent molecular fluxes. For example, we theoretically derive and experimentally verify accurate circuit models for Michaelis-Menten kinetics. Then, we show that such circuit models can be scaled via simple wiring among circuit motifs to represent more and arbitrarily complex reactions. Hence, we can directly map reaction networks to equivalent circuit schematics in a rapid, quantitatively accurate, and intuitive fashion without needing mathematical equations. We verify experimentally that these circuit models are quantitatively accurate. Examples include 1) different mechanisms of competitive, noncompetitive, uncompetitive, and mixed enzyme inhibition, important for understanding pharmacokinetics; 2) product-feedback inhibition, common in biochemistry; 3) reversible reactions; 4) multi-substrate enzymatic reactions, both important in many metabolic pathways; and 5) translation and transcription dynamics in a cell-free system, which brings insight into the functioning of all gene-protein networks. We envision that circuit modeling and simulation could become a powerful scientific communication language and tool for quantitative studies of kinetics in biology and related fields.